Pooling of specimens to increase efficiency of screening individuals for rare diseases has a long history, dating back to screening of syphilis in military inductees in the 1940s. Over the years, specimen pooling or group testing has been applied in entomology, genetics, international trade, the pharmaceutical and blood bank industries, computer science, analytical chemistry, and many other areas. In the context of infectious diseases, group testing is typically used for (i) case identification, i.e., detecting all individuals having the disease of interest, and (ii) prevalence estimation, i.e., estimating the proportion of individuals in the population having a particular disease. As an example of the former, currently the nation's blood banks and the state of North Carolina's public HIV testing system employ specimen pooling to detect individuals recently infected with HIV. Group testing can be more efficient, accurate, and precise than individual testing; yet, many of the results supporting the use of group testing have gone unrecognized in the infectious disease setting owing to the vast and diverse group testing literature. We propose to research statistical aspects of group testing with application to case identification and prevalence estimation in HIV/AIDS and other infectious diseases. Our specific aims are to derive and compare the operating characteristics of multistage and array based group testing algorithms for both the identification and estimation problems in the presence of test error. Models for test error will consider constant and pool size dependent sensitivity and specificity. We hypothesize that array testing algorithms are superior to conventional multistage testing algorithms in terms of efficiency and accuracy. While the methodology is motivated by detection of acute HIV, our results will be directly applicable to other infectious diseases. [unreadable] [unreadable]